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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

共 557 条记录，以下是161-170 订阅

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Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 18

Hierarchical Deep Network for Group Discovery and Multi-leve...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Ashish Goyal Neha Bhargava Subhasis Chaudhuri Rajbabu Velmurugan Indian Institute of Technology Bombay Mumbai Maharashtra

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. the group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. these modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

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Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian 18

Seek and You Will Find: A New Optimized Framework for Effici...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sudip Das Partha Sarathi Mukherjee Ujjwal Bhattacharya Indian Statistical Institute Kolkata India

ISBN: (纸本)9781450366151

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16% improvement over the existing state-of-the-art result.

关键词： Convolutional Neural Networks

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Targeted Change Detection in Remote Sensing images 11

Targeted Change Detection in Remote Sensing Images

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11th International conference on Machine vision (ICMV)

作者： Ignatiev, V. Trekin, A. Lobachev, V. Potapov, G. Burnaev, E. Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (数字)9781510627499

ISBN: (纸本)9781510627499

Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images ( so called targeted change detection). In this paper we develop a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.

关键词： remote sensing change detection satellite imagery computer vision deep learning

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Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition: SRC-CNN for Scale and Rotation Invariant Character Recognition 2018

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and R...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Swetha V. C. Deepak Mishra Sai Subrahmanyam Gorthi Indian Institute of Space Science and Technology Thiruvananthapuram Kerala Indian Institution of Technology Tirupati Andra Pradesh

ISBN: (纸本)9781450366151

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. the self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. this paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. the proposed method does not demand any training with samples which are scaled or rotated. the performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. this paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification.

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Video Dehazing using LMNN with respect to Augmented MRF 18

Video Dehazing using LMNN with respect to Augmented MRF

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Kushal Borkar Snehasis Mukherjee Indian Institute of Information Technology Sricity Chittoor Andhra Pradesh

ISBN: (纸本)9781450366151

the presence of haze within the atmospheric medium degrades the quality of videos captured by camera sensors. the expulsion of haze, referred to as dehazing, is typically performed subject to the physical degradation display, that involves an explanation of an ill-posed inverse drawback. A few efforts have been made for image dehazing, whereas, video dehazing still remains an unexplored area of research. this paper proposes an approach for video dehazing combining the concepts of single image dehazing, optical stream estimation and Markov Random Field (MRF). the proposed method enhances the temporal and spatial coherence of the hazy video. Assuming that the dark channel of the haze-free picture is zero, we acquire the raw transmission map. In the proposed approach, we focus on the raw transmission map obtained from the dark channel prior using guided filter. We assess the forward and reverse optical streams between the neighboring frames to locate individual pixels using Linear Discriminant Analysis. the color of the haze-free pixels in the frames is approximated by a few hundred discrete colors, which generate a fixed cluster in space and the directions of the pixel. the pixels at a given cluster are spread and can be determined by analyzing the forward and in reverse optical frames to predict its value after haze removal. Largest Margin Nearest Neighbor (LMNN) algorithm is applied to get the smooth transmission map of the foggy frames of the video to approximate the pixel value in the RGB space. the stream fields are utilized in an augmented MRF model on the transmission guide obtained to enhance the temporal and the spatial coherence of the transmission. the proposed method is compared against the state-of-the-art on both real and synthetic videos to preserve the information optimally.

关键词： bayes

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Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion 4th

Word-Wise Handwriting Based Gender Identification Using Mult...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. this paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. this process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image segmentation

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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral imagery and Dual-polarimetric SAR Data 18

Unsupervised Detection of Surface Mine Sites using Sentinel ...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Shashaank M. Aswatha Vishnu Saini Jayanta Mukherjee Prabir K. Biswas Subhas Aikat Arundhati Misra Indian Institute of Technology Kharagpur West Bengal India Indian Institute of Technology Kharagpur India Space Applications Centre Indian Space Research Organization Ahmedabad India

ISBN: (纸本)9781450366151

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. these features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. the average detection accuracy in our results is found to be around 94.8%.

关键词： Multi-spectral imagery

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An Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script 4th

An Efficient Multi Lingual Optical Character Recognition Sys...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Chakravarthy, Srinivasa Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India Indian Institute of Technology-Madras Chennai India

ISBN: (纸本)9789811393600

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for indian languages. the language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script (*** ) based OCR system in which the inherent drawbacks of indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. the proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for indian scripts. the proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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A Deep Learning Architecture Based Dimensionality Reduction and Online Signature Verification 4th

A Deep Learning Architecture Based Dimensionality Reduction ...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India

ISBN: (纸本)9789811393600

In this paper, we propose a novel hybrid deep learning based autoencoder-CNN-Softmax architecture aims at obtaining reduced dimension feature set from raw feature set. the reduced feature set forms an input to CNN layers to learn deep global features. these global features are used to train the SoftMax layer for online signature classification. Ability to reduce the noisy features and to discover the hidden corelated features makes the proposed architecture light weight and efficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint mobile devices. We demonstrate the superiority of our model for feature correlation learning and signature classification by conducting experiments on standard datasets MCYT, SUSIG. the experimentation confirms that the proposed model achieves better accuracy (lower error rates) with a lesser number of features compared to the current state-of-the-art models. the proposed models yield state-of-the-art performance of 0.4% EER on MCYT-100 dataset and 3.47% with SUSIG dataset. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Leveraging information from imperfect examples: Common action sequence mining from a mix of incorrect performances 18

Leveraging information from imperfect examples: Common actio...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Hiteshi Jain Gaurav Harit Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9781450366151

As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos depicting action by human experts are required. Lack of experts in any domain leads to reduced number of videos and hence an improper learning. In this work we attempt to utilize imperfect amateur performances to get more confident representations of human action sequences. We introduce a novel Community Detection based unsupervised framework that provides mechanisms to interpret video data and address its limitations to produce better action representation. Human actions are composed of distinguishable key poses which form dense communities in graph structures. Anomalous poses performed for a longer duration can also form such dense communities but can be identified based on their rare occurrence across action videos and be rejected. Further, we propose a technique to learn the temporal order of these key poses from these imperfect videos, where the inter community links help reduce the search space of many possible pose sequences. Our framework is seen to improve the segmentation performance of complex human actions with the help of some imperfect performances. the efficacy of our approach has been illustrated over two complex action datasets - Sun Salutation and Warm-up exercise, that have been developed using random executions from amateur performers.

关键词： Unsupervised Anomaly Detection

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